Journal
BMC INFECTIOUS DISEASES
Volume 12, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1471-2334-12-154
Keywords
Skin infection; Prediction rule; Clinical decision; MRSA; Hospitalization
Categories
Funding
- Astellas Pharma US (APUS), Inc, Deerfield, IL
- APUS
- Cerner Corporation
Ask authors/readers for more resources
Background: Methicillin-resistant Staphylococcus aureus (MRSA) is a frequent cause of complicated skin and skin structure infections (cSSSI). Patients with MRSA require different empiric treatment than those with non-MRSA infections, yet no accurate tools exist to aid in stratifying the risk for a MRSA cSSSI. We sought to develop a simple bedside decision rule to tailor empiric coverage more accurately. Methods: We conducted a large multicenter (N=62 hospitals) retrospective cohort study in a US-based database between April 2005 and March 2009. All adult initial admissions with ICD-9-CM codes specific to cSSSI were included. Patients admitted with MRSA vs. non-MRSA were compared with regard to baseline demographic, clinical and hospital characteristics. We developed and validated a model to predict the risk of MRSA, and compared its performance via sensitivity, specificity and other classification statistics to the healthcare-associated (HCA) infection risk factors. Results: Of the 7,183 patients with cSSSI, 2,387 (33.2%) had MRSA. Factors discriminating MRSA from non-MRSA were age, African-American race, no evidence of diabetes mellitus, cancer or renal dysfunction, and prior history of cardiac dysrhythmia. The score ranging from 0 to 8 points exhibited a consistent dose-response relationship. A MRSA score of 5 or higher was superior to the HCA classification in all characteristics, while that of 4 or higher was superior on all metrics except specificity. Conclusions: MRSA is present in 1/3 of all hospitalized cSSSI. A simple bedside risk score can help discriminate the risk for MRSA vs. other pathogens with improved accuracy compared to the HCA definition.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available